Automatic Speech Recognition System for Dialectal Variations in Arabic Language.

Thesis Abstract

Automatic Speech Recognition (ASR) systems play a crucial role in facilitating human-computer interaction by enabling machines to understand and process spoken language. However, the performance of ASR systems can be significantly affected by dialectal variations in languages such as Arabic, which is spoken across a wide geographic region with diverse linguistic features. This thesis presents an in-depth exploration of the challenges posed by dialectal variations in Arabic language for ASR systems and proposes novel approaches to address these challenges. The introductory section of the thesis provides a comprehensive overview of the research problem, highlighting the importance of developing ASR systems that can accurately recognize and transcribe dialectal variations in Arabic. The background of the study delves into the existing literature on ASR systems and dialectal variations in Arabic, laying the foundation for the research objectives. The problem statement identifies the specific challenges faced by ASR systems in handling dialectal variations, while the objectives of the study outline the goals and aims of the research. The limitations of the study are also discussed, acknowledging the constraints and potential areas for future research. The scope of the study defines the boundaries and focus of the research, while the significance of the study highlights the potential impact and contributions of the research findings. The structure of the thesis provides a roadmap for the reader, outlining the organization of the chapters and sections. The literature review in Chapter Two critically examines existing research on ASR systems and dialectal variations in Arabic, identifying key trends, challenges, and gaps in the literature. The research methodology in Chapter Three describes the approach and methods used to develop and evaluate the proposed ASR system for handling dialectal variations in Arabic. This includes data collection, preprocessing, feature extraction, model training, and evaluation procedures. Chapter Four presents a detailed discussion of the findings, including the performance evaluation of the developed ASR system on different Arabic dialects. The chapter analyzes the strengths and limitations of the proposed system, highlighting key insights and implications for future research. Finally, Chapter Five offers a comprehensive conclusion and summary of the thesis, summarizing the key findings, contributions, and recommendations for further research. Overall, this thesis contributes to the field of ASR research by addressing the challenges posed by dialectal variations in Arabic language and proposing innovative solutions to improve the accuracy and robustness of ASR systems. By developing a deeper understanding of dialectal variations and their impact on ASR performance, this research aims to advance the development of more effective and reliable ASR systems for Arabic speakers across different dialectal backgrounds.